最核心的内容,就是作者提出的Cross-Image Attention。这个东西是什么呢?请看下图: 图2 我们简单总结一下,给定structure image和一个appearance image,我可以令K,V是Appearance Image,Q是structure image,然后他们做常规的Self-attention操作,可以发现,两张不同的图,长颈鹿和斑马的语义是能够对应起来的,比如脖子对脖子,...
相比于直接text2image生成,text-guided editing要求原来图像绝大部分区变化不大,目前的方法需要用户指定mask来引导生成。 本文发现 cross-attention对于image的布局控制很重要。 目前已有的纯text-guided的editing(text2live)text2live,目前只能修改图片的纹理(外观),不能修改复杂的实体结构,比如把自行车换成一辆车。并且,...
In order to tackle these challenges, we propose a noveluncertain area attention and cross-image context extraction network for accuratepolyp segmentation, which consists of the uncertain area attention module(UAAM), the cross-image context extraction module (CCEM), and theadaptive fusion module (AFM...
This unique cross-attention transformer processes pairs of images as input, enabling intricate cross-attention operations that delve into the interconnections and relationships between the distinct features in the two images. Through meticulous iterations of Cross-ViT, we assess the ranking capabilities of...
Official Pytorch implementation of Dual Cross-Attention for Medical Image Segmentation - gorkemcanates/Dual-Cross-Attention
Image-text Cross-modal Matching Method Based on Stacked Cross Attention Cross-modal matching of image-text is an important task in the intersection of computer vision and natural language processing. However, traditional image-... HongbinWANG,ZhiliangZHANG,HuafengLI - 《Journal of Signal Processing》...
Stacked Cross Attention 是一种注意力机制,它在处理多模态数据(如图像和文本)时,能够捕捉不同模态间的交互信息。这种机制通过在多个层级上堆叠注意力模块,逐步深化对跨模态信息的理解和融合。每个注意力模块都会根据前一层的输出,重新计算不同模态元素之间的相关性权重,从而实现对关键信息的聚焦。 2. 阐述Stacked Cro...
1. Stacked Cross Attention Network 本文提出了一个 Stacked Cross Attention Network 将 words 和 image regions 映射到一个共同的 embedding space 来预测整张图和一个句子之间的相似性。作者首先用 bottom-up attention 来检测和编码图像区域,提取其 feature。与此同时,也对 word 进行单词映射。然后用 Stacked Cr...
Attention Control allows much finer control of the prompt by modifying the internal attention maps of the diffusion model during inference without the need for the user to input a mask and does so with minimal performance penalities (compared to clip guidance) and no additional training or fine-...
Cross-attention mechanismWith the advancement of Vision Transformer (ViT) in remote sensing image change detection, the most popular feature extraction methods involve using pre-trained ResNet or VGG networks. However, due to differences between the pre-trained datasets and remote sensing datasets, ...